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Infrared Small Target Detection Fusion Network based on singular value decomposition and Rayleigh feature amplifier
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DOI：10.1016/j.infrared.2025.106353.png)
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Abstract
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Recent advancements in deep learning-based detection methods have led to substantial improvements in the performance of infrared small target detection (IRSTD). However, most methods rely solely on the original image and overlook the valuable prior knowledge it contains. As a result, these methods exhibit limited capability in extracting and enhancing target features. To tackle this problem, a fusion framework named the Infrared Small Target Detection Fusion Network (ISTDFN) has been developed, consisting mainly of a decomposition network and a detection network. Initially, to guide the network’s focus toward target features, we use the decomposition network to factorize images into sparse and low-rank components. The target information will primarily be contained in the sparse components. Furthermore, to improve the efficiency of feature extraction, the detection network incorporates a feature amplifier based on the Rayleigh distribution, which is inspired by the thermal diffusion model of small targets in physics, enabling dynamic enhancement of target features. Finally, after extracting and enhancing features from the two decomposed components, a singular value feature fusion (SVFF) module is applied in the detection network to facilitate effective information interaction and fusion. Comprehensive experiments demonstrate that the proposed approach can accurately and efficiently detect targets, even under challenging background conditions.
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